Whole-Process Privacy-Preserving and Sybil-Resilient Consensus for Multiagent Networks.

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yiming Wu, Chenduo Ying, Ning Zheng, Wen-An Zhang, Shanying Zhu
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引用次数: 0

Abstract

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

多代理网络的全流程隐私保护和假人弹性共识。
在多代理网络(MANs)中,代理之间通过通信网络进行的信息交流受到窃听和 Sybil 攻击,本文关注为这类网络共同设计隐私保护和弹性共识协议。首先,我们引入了一种新的攻击模型,即敌对代理可以发起仿真攻击,在网络中生成大量虚假实体,从而获得不成比例的影响力。在这个通信框架中,我们设计了一种全过程隐私保护机制,能够保护代理的初始状态和当前状态。然后,与现有的需要识别和缓解假节点的方法不同,我们实施了一种基于度的均值-序列-缩减(D-MSR)弹性策略,展示了它的重要特性:1) 确保上述设计的隐私保护策略的有效性;2) 允许网络包含假冒节点而不被消除;3) 在正常代理之间达成共识。最后,提供了几个数值模拟来验证所提结果的有效性。
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来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
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